Args:

min_after_dequeue: Minimum number elements in the queue after a
dequeue, used to ensure a level of mixing of elements.

keep_input: A bool Tensor. This tensor controls whether the input is
added to the queue or not. If it is a scalar and evaluates True, then
tensors are all added to the queue. If it is a vector and enqueue_many
is True, then each example is added to the queue only if the
corresponding value in keep_input is True. This tensor essentially
acts as a filtering mechanism.

num_threads: The number of threads enqueuing tensor_list.

seed: Seed for the random shuffling within the queue.

enqueue_many: Whether each tensor in tensor_list is a single example.

shapes: (Optional) The shapes for each example. Defaults to the
inferred shapes for tensor_list.

allow_smaller_final_batch: (Optional) Boolean. If True, allow the final
batch to be smaller if there are insufficient items left in the queue.

shared_name: (Optional) If set, this queue will be shared under the given
name across multiple sessions.

name: (Optional) A name for the operations.

Returns:

A list or dictionary of tensors with the types as tensors.

Raises:

ValueError: If the shapes are not specified, and cannot be
inferred from the elements of tensors.

Eager Compatibility

Input pipelines based on Queues are not supported when eager execution is
enabled. Please use the tf.data API to ingest data under eager execution.